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Disturbance observer based adaptive model predictive control for uncalibrated visual servoing in constrained environments

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ISA TRANSACTIONS 2020年 106卷 40-50页

作者： Qiu, Zhoujingzi Hu, Shiqiang Liang, Xinwu Sun Yat Sen Univ Sch Aeronaut & Astronaut Guangzhou Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai Peoples R China

This paper presents an adaptive model predictive control (MPC) method based on disturbance observer (DOB) to improve the disturbance rejection performance of the image-based visual servoing (IBVS) system. The proposed control method is developed based on the depth-independent interaction matrix, which can simultaneously handle unknown camera intrinsic and extrinsic parameters, unknown depth parameters, system constraints, as well as external disturbances. The proposed control scheme includes two parts which are the feedback regulation part based on the adaptive MPC and the feedforward compensation part based on the modified DOB. Unlike the traditional DOB that is based on the fixed nominal plant model, the modified DOB here is based on the estimated plant model. The adaptive MPC controller consists of an iterative identification algorithm, which not only can provide the model parameters for both the controller and the modified DOB, but also can be used to control plant dynamics and to minimize the effects of DOB. Simulations for both the eye-in-hand and eye-to-hand camera configurations are conducted to illustrate the effectiveness of the proposed method. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Visual servoing Depth-independent interaction matrix adaptive model predictive control Modified disturbance observer Disturbance rejection

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adaptive model predictive control with Successive Linearization for Distillate Composition control in Batch Distillation 5

Adaptive Model Predictive Control with Successive Linearizat...

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Moratuwa Engineering Research Conference (MERCon) / 5th International Multidisciplinary Engineering Research Conference

作者： Mendis, Pramuditha Wickramasinghe, Chathuranga Narayana, Mahinsasa Bayer, Christoph Univ Moratuwa Dept Chem & Proc Engn Moratuwa Sri Lanka TH Nurnberg Georg Simon Ohm Proc Engn Nurnberg Germany

ISBN: (纸本)9781728136325

This paper investigates the application of adaptive model predictive control (MPC) with successive linearization for the control of top product purity of a batch distillation column. adaptive MPC with successive linearization can overcome the prediction inaccuracies associated with linearization of highly non-linear and dynamic mathematical model of a batch distillation column, with a lower computational load than non-linear MPC. A binary mixture of methanol and water was selected to demonstrate the controller development, and its performance was investigated by varying MPC tuning parameters in the MATLAB/Simulink simulation environment. Results indicated that the choice of tuning parameters had a considerable influence on the MPC's ability to track a constant set-point for the output. With the correct choice of tuning parameters, however, it is possible for the controller to track a constant set-point. The present approach is compared with nonlinear MPC in order to gain a quantitative understanding on accuracy and computational effort.

关键词： Batch distillation adaptive model predictive control Successive linearization

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A Comparison of Two Methods of adaptive Nonlinear model predictive control

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IFAC-PapersOnLine 2024年第25期58卷 90-95页

作者： A. Bamimore D.A. Akomolafe P.J. Asubiaro A.S. Osunleke

Conventional nonlinear model predictive control (NMPC) relies on an accurate process model. However, real-world systems’ models are often imperfect due to parametric variations, modelling errors and additive noise, leading to degraded control performance. This study investigates the effectiveness of two adaptive NMPC methods in solving this problem, namely, linear parameter varying (LPV) model predictive control (LPV-MPC) and model predictive control based on successive linearization (SL-MPC). In addition, an innovative approach for identifying LPV models is proposed and applied to three simulation examples. The identified LPV models gave a very strong fit. Also, simulation results demonstrate that both adaptive predictive NMPC (LPV-MPC and SL-MPC) exhibit performance similar to conventional NMPC and superior to Linear MPC. Notably, the two adaptive predictive controllers offer significantly reduced computational time compared to conventional NMPC.

关键词： adaptive model predictive control linear parameter varying (LPV) model nonlinear predictive control Linear time varying (LTV) model

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Design of adaptive model predictive control for a class of uncertain non-linear dynamic systems: stability, convergence, and robustness analysis

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IET control THEORY AND APPLICATIONS 2019年第15期13卷 2376-2386页

作者： Bolandi, Hossein Saki, Saman Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

Aside from relying on the robustness of the model predictive control (MPC) against model uncertainties, tracking performance degradation in the transient time is an inevitable challenge which is the main motivation of the adaptive MPC (AMPC). To that aim, in this study, a fast online identification scheme is suggested to identify a suitable linear state-space model used in the control algorithm. Then, the authors prove the convergence of the identification error sequence using Lyapunov candidate function in the time domain and also, in the frequency domain (graph topology) point of view. Also, due to the dependency of the convergence error on the initial selection of the identification model, the authors introduce a stability and convergence ball as a function of generalised stability margin and the v-gap criteria. Then, the stability and feasibility of the proposed AMPC is guaranteed when using the identified linear model at each sampling time. Moreover, the identification error convergence applies a constraint on the MPC in which, with a suitable selection of the update rate, MPC cost function is been relaxed from the constraint. In the simulations, the authors consider two examples of highly non-linear dynamic systems. In both cases the proposed control strategy gives satisfactory conclusions.

关键词： convergence identification predictive control linear systems uncertain systems robust control closed loop systems control system synthesis adaptive control state-space methods Lyapunov methods nonlinear dynamical systems identification model generalised stability margin AMPC identified linear model sampling time identification error convergence suitable selection MPC cost function nonlinear dynamic systems control strategy adaptive model predictive control robustness analysis model uncertainties performance degradation transient time inevitable challenge adaptive MPC fast online identification scheme suitable linear state-space model control algorithm identification error sequence Lyapunov candidate function time domain graph topology convergence error

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Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems

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JOURNAL OF PROCESS control 2019年 77卷 97-113页

作者： Hajizadeh, Iman Rashid, Mudassir Cinar, Ali IIT Dept Chem & Biol Engn Chicago IL 60616 USA IIT Dept Biomed Engn Chicago IL 60616 USA

An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closed-loop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： adaptive model predictive control Recursive subspace identification Dynamic constraint adjustment Artificial pancreas system Plasma insulin concentration Insulin on board

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adaptive model predictive control-based attitude and trajectory tracking of a VTOL aircraft

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IET control THEORY AND APPLICATIONS 2018年第15期12卷 2031-2042页

作者： Emami, Seyyed Ali Rezaeizadeh, Amin Sharif Univ Technol Dept Aerosp Engn Tehran Iran Sharif Univ Technol Dept Elect Engn Tehran Iran

A novel adaptive model-based predictive controller for attitude and trajectory tracking of a vertical take-off and landing (VTOL) aircraft in the simultaneous presence of model uncertainties and external disturbances is introduced in this study. An important challenge of designing the model-based controllers is developing an accurate model, especially in the presence of model uncertainties. In this study, first, the nominal model of a ducted-fan air vehicle, which is a multi-input multi-output nonlinear system with non-minimum phase behaviour, is given as the test case of this research. After that, two modified robust and adaptive model predictive controllers are proposed for tracking a predefined path in the presence of external gusts and model uncertainties. The design is performed by introducing two novel observers to estimate the model uncertainties and external disturbances, simultaneously. Different types of input constraints, as well as the time delay of the system inputs, can be considered effectively in the design steps of the proposed control strategy. The stability of the closed-loop system is proved under a few realistic assumptions. The simulation results demonstrate the great capability of the proposed method for attitude and trajectory tracking of the VTOL aircraft under model uncertainties and wind turbulence.

关键词： predictive control attitude control stability closed loop systems observers nonlinear control systems linear systems control system synthesis robust control aircraft control adaptive control aerospace control model uncertainties external disturbances adaptive controller trajectory tracking VTOL aircraft adaptive model predictive control predictive controller (VTOL) aircraft nominal model multiinput multioutput nonlinear system

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Dynamic obstacle avoidance of unmanned ship based on event-triggered adaptive nonlinear model predictive control

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OCEAN ENGINEERING 2023年第Part2期286卷

作者： Li, Shilong Zhu, Yakun Bai, Jianguo Guo, Ge Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

This article studies trajectory tracking and dynamic obstacle avoidance problems of unmanned ships, and an event-triggered adaptive nonlinear model predictive control (EANMPC) method is constructed to solve the above problems. Firstly, a novel adaptive nonlinear model predictive control is used to track the trajectory of the unmanned ship. And then, an obstacle avoidance mechanism is formulated to deal with the condition of dynamic obstacles that appear during the unmanned ship trajectory tracking process. Secondly, an event triggered mechanism is established to reduce the computational burden of the controller. Finally, several simulation results are provided for demonstrating the effectiveness and advantage of EANMPC.

关键词： Dynamic obstacle avoidance Trajectory tracking Event-triggered adaptive model predictive control

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adaptive model predictive control with propulsion load estimation and prediction for all-electric ship energy management

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ENERGY 2018年 150卷 877-889页

作者： Hou, Jun Sun, Jing Hofmann, Heath Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

Electric ships experience large propulsion-load fluctuations on their drive shaft due to encountered waves and the rotational motion of the propeller, affecting the reliability of the shipboard power network and causing wear and tear. To address the load fluctuations, model predictive control has been explored as an effective solution. However, the load torque of the propulsion system, knowledge of which is essential for model predictive control, is difficult to measure and includes multi-frequency fluctuations. To deal with this issue, an adaptive model predictive control is developed so that the load torque estimation and prediction can be incorporated into model predictive control. In order to evaluate the effectiveness of the proposed adaptive model predictive control, an input observer with linear prediction is developed as an alternative approach to obtain the load estimation and prediction. Comparative studies are performed to illustrate the importance of the load torque estimation and prediction, and demonstrate the effectiveness of the proposed adaptive model predictive control in terms of improved efficiency, enhanced reliability and reduced wear and tear. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Propulsion-load torque estimation and prediction adaptive model predictive control All-electric ship Hybrid energy storage Energy management strategy

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adaptive model predictive control for hybrid energy storage energy management in all-electric ship microgrids

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ENERGY CONVERSION AND MANAGEMENT 2019年 198卷 111929-000页

作者： Hou, Jun Song, Ziyou Hofmann, Heath Sun, Jing Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

Hybrid energy storage systems have been widely used in transportation, microgrid and renewable energy applications to improve system efficiency and enhance reliability. However, parameter uncertainty can significantly affect system performance. In order to address this issue, an adaptive model predictive control is developed in this paper. Online parameter identification is used to mitigate parameter uncertainty, and model predictive control is used to optimally split power, deal with constraints, and achieve desired dynamic responses. A sensitivity analysis is conducted to identify major impact factors. In order to validate the proposed method, both simulation and experiments are performed to show the effectiveness of the proposed adaptive model predictive control. Compared to the model predictive control without online parameter identification, the power loss reduction can be as high as 15% in the experiments. This study focuses on all-electric ship energy management to mitigate load fluctuations and improve system efficiency and reliability. The proposed method could also be used in other applications.

关键词： All-electric ships Energy management adaptive model predictive control Parameter uncertainties Hybrid energy storage

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A novel adaptive model predictive frequency control using unscented Kalman filter

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期213卷

作者： Wang, Weichao Yorino, Naoto Sasaki, Yutaka Zoka, Yoshifumi Bedawy, Ahmed Kawauchi, Seiji Hiroshima Univ Grad Sch Adv Sci & Engn Hiroshima Japan Kure Coll Natl Inst Technol KOSEN Kure Japan Chugoku Elect Power Co Inc Energia Res Inst Higashihiroshima Japan

This paper proposes a novel adaptive load frequency control (LFC) method based on model predictive control (MPC) using an unscented Kalman filter (UKF). The objective is to develop a robust LFC controller against the change in frequency characteristics of a power system. An adaptive MPC (AMPC) control is realized by updating the prediction model on-line to capture the frequency characteristics of a target system. A simplified prediction model is identified in the preliminary examination of the closed-loop performance of the target system, and then the system parameters are estimated in real-time circumstances. The estimated parameters include equivalent inertia of the target system. An interconnected three-area test system is used in the simulations to confirm the effectiveness of the proposed approach. The simulation results verify that the proposed MPC method can work successfully and efficiently handle the disturbances of the power system. The successful results also confirm that the proposed control scheme can be applied to a microgrid (MG) project, which is being promoted by the authors.

关键词： Power systems Load frequency control adaptive model predictive control Unscented Kalman filter

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